Fine particulate matter (PM2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM2.5 pollution in the study area, the PM2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM2.5, while elevation makes a strong negative impact on PM2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin.
In the context of rapid urbanization, the phenomenon of spatial fragmentation in Chinese inland central cities is significant. The scientific measurement and evaluation of urban spatial fragmentation are conducive to its transformation, advancement, and sustainable development. Based on the fractal dimension index and Shannon index, this study measures urban spatial fragmentation in terms of form and function, respectively. In addition, multi-scale geographic weighted regression (MGWR) is used to study the influencing factors of spatial fragmentation. The conclusions are as follows: ① the measurement results of spatial form fragmentation and functional fragmentation of urban built-up areas are consistent. The fragmentation degree of the new urban area (new urban district and high-tech district) is higher than that of the old urban areas, and the urban space fragmentation degree around railways and rivers is high. The urban space fragmentation degree of coal resource concentrated distribution areas in the north is lower. The cold spot area of the fragmentation phenomenon appears in the old urban area, and the hot spot area is in the new urban area and along the railway. ② The positive influencing factors of urban spatial fragmentation in Pingdingshan city are the NDVI and the distance from CBD. The negative influencing factor is the number of bus stops per unit area. The DEM and population density have no significant impact on urban fragmentation in Pingdingshan city. ③ Among the variables with significance, its influence has a certain spatial heterogeneity. The spatial scale from small to large is the number of bus stops per unit area, NDVI, and the distance from CBD. The degree of urban fragmentation is very sensitive to the number of bus stops per unit area and the impact scale is quite small. The spatial impacts of the NDVI and the distance from CBD are relatively stable. This study provides a reference and basis for the spatial development of built-up areas of inland central cities and promotes the transformation, advancement, and sustainable development of inland central cities.
This study aims to investigate the spatial associations of luxury hotels by using geographical information system (GIS) tools and the multiscale geographically weighted regression (MGWR) model to examine the relationships between the distribution of luxury hotels and exogenous (regional) determinants of urban subdistricts in which the luxury hotels are located. Shanghai City is used as an example. The study first introduces the spatial-temporal characteristics of luxury hotels in Shanghai City, and the key exogenous determinants that contribute to luxury hotel location choice are identified with the MGWR model. The nearest neighbor index decreased from 1.01 to 0.47 and Moran’s I statistics increased from 0.268 to 0.452, revealing that the spatial-temporal evolution pattern of luxury hotels presents a cluster trend from 1995 to 2015. The significance level of the standard regression coefficient shows that the institutional proximity, room rate, green space and the World Expo are the primary determining factors that influence the distribution of luxury hotels in Shanghai City. The analysis is important theoretically, as it presents new and novel methodologies for shedding light on the influencing factors of the locational dynamics of luxury hotels. Meanwhile, it enriches the methodologies for analyzing the relationships between luxury hotels and urban structures, and it is important for practitioners, as it provides strategic information that would enable them to globally select appropriate locations for luxury hotels.
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